Several people have approached me to ask if it is possible to use coding in a WPR analysis. Coding in this context refers to the labelling of discrete items in research materials (contrast “learning to code” in Research Hub, 30 Oct 2023). The espoused goal is to find a way to organize a large mass of material, which seems unmanageable if one simply applies the WPR questions. The topic addressed here is relevant to earlier Research Hub entries on data, where I ask specifically about the place of data in a WPR analysis (30 May 2022, 29 June 2022, 29 Aug 2022). 

The most common way to approach qualitative studies that involve large amounts of material is thematic analysis. I have on occasion positioned WPR as opposed to or in contrast with thematic analysis. I wish to explain the basis of this claim and then to proceed to consider what RTA (reflexive thematic analysis) may offer WPR researchers. 

The title of this entry refers to my recent article introducing WPR to the study of music education where I list several “themes” and organize the paper around them (Bacchi 2023). The term “themes” is used incorrectly in this paper – hence the “oops”! As Braun and Clarke explain, I should have referred to “topics” rather than to “themes”. To qualify as a theme, the selected items would need to capture “some level of patterned response or meaning” (Braun and Clarke 2021: 341). I pursue this distinction between “topics” and “themes” later. 

On what basis have I distanced WPR from thematic analysis? 

To preview the discussion to follow, I contrast the types of analysis performed by RTA and WPR. The former seeks to identify themes; the latter identifies and interrogates problem representations. In RTA, themes are “picked out” by researchers; in WPR problem representations form part of an analytic strategy. 

Note, I am not saying that themes simply emerge from the data. Braun and Clarke (2021: 343) make it very clear that they have never said that themes simply emerge from the data:

An account of themes “emerging” or being “discovered” is a passive account of the process of analysis, and it denies the active role the researcher always plays in identifying patterns/themes, selecting which are of interest, and reporting them to the readers (Taylor and Ussher, 2001) 

They justifiably correct St Pierre for her characterisation of thematic analysis as a form of analysis “in which themes somehow miraculously emerge from the data” (St. Pierre 2019: 4). Rather, Braun and Clarke (2006: 80) have consistently identified themes as part of an interpretive process involving the researcher. 

My concern is that inadequate attention is paid to the central role played by research questions in this interpretive process. That is, RTA and WPR ask distinctly different forms of research question, informed by specific theoretical premises (see below). This contrast in theoretical perspective makes it difficult to bring the two approaches together. 

Braun and Clarke (2006: 95) attest to the powerful role played by research questions in their comments on a possible “mismatch between theory and analytic claims”: 

“if you are working within an experiential framework, you would typically not make claims about the social construction of the research topic, and if you were doing constructionist thematic analysis, you would not treat people’s talk of experience as a transparent window on their world.”

A recent worked example of RTA by Byrne (2022) illustrates the close ties between “themes” and one’s research questions. Likewise, Braun and Clarke (2006: 88) emphasize the goal of identifying what is “interesting” in the material, driven clearly by the researcher’s research questions. For this reason, researchers are enjoined to “spell out” their theoretical assumptions. 

I take this insight one step further. If research questions “manage” or “control” the analysis to the extent just described – if one’s research questions shape the analysis – it follows that the common distinction drawn between “inductive” (“data-driven”) and “deductive” (“analyst-driven”) accounts seems spurious (Braun and Clarke 2006: 83-84). Since all researchers are necessarily engaged in asking research questions and, since all research questions are theoretically informed, all research is “analyst-driven”. And, if this is the case, the selection of codes and the naming of themes are necessarily shaped by the research questions.

Following this reasoning, the kinds of research questions posed in any study play a prominent role in the kind of analysis produced. It is at this level that I detect a tension between thematic analysis and WPR. 

The politics of research questions

Alvesson and Sandberg (2013) provide a useful guide to the contrasting theoretical premises in varieties of research questioning.

They show, for example, how research questions problematise different sorts of assumptions underlying existing literatures. Their typology of assumptions includes: in-house assumptions, root metaphor assumptions, paradigmatic assumptions, ideological assumptions and “field assumptions” (2013: 54). To generate research questions “the focal point in problematization as a methodology” is “to illuminate and challenge those assumptions underlying existing theories (including one’s own favorite theories) about a specific subject matter” (Alvesson and Sandberg 2013: 53; emphasis in original). 

As an example of an in-house assumption, Alvesson and Sandberg (2013: 54) explain how researchers who question the status of certain characteristics as leadership traits, remain, theoretically, within “trait theory” as a theoretical proposition. “Trait theory”, as a framing logic, is considered to be uncontroversial. It is simply not mentioned. 

This example illustrates that the analysis (of leadership “traits”) necessarily reflects the researcher’s theoretical positioning and hence their research questions. In this sense, research questions shape what will be considered relevant. Trying to identify “themes” through coded terms will tell us little if the researcher’s own assumptions are not considered. 

Braum and Clarke (2006: 78) stress the need for researchers to “make their (epistemological and other) assumptions explicit”. However, the central role of research questions in shaping the parameters of what is analysed is underdiscussed. The framework for analysis is generated through research questions that are based upon assumed theoretical premises. It follows that the very different kinds of research questions produced by WPR and RTA means that blending the two forms of analysis faces significant obstacles.

Comparing research questions

WPR asks questions about problematizations (or problem representations), and these are tied to the specifics of practical texts (Bacchi 2009: 34). That is, to initiate a WPR analysis, the researcher identifies “proposals” or “proposed solutions” in policy and other forms of “text” and works backwards to draw attention to the implicit problem representations they produce. “Proposals” are taken from “practical” or “prescriptive texts” that provide guides to conduct. By telling us what to do, the proposals within “texts” indicate what needs to change and hence what is enacted as “the problem”. In this way WPR offers an analytic strategy built upon problematisations. The questions a WPR researcher asks will invariably be questions about problematisations, their presuppositions, origins and effects. 

Thematic analysis (and Reflexive Thematic Analysis) starts from different kinds of questions. While Braun and Clarke (2006: 77 Abstract) argue that RTA is a useful strategy in disciplines “beyond psychology”, the kinds of questions asked tend to presume the existence of states of being or states of mind. Byrne (2022) for example focuses on deciphering the “opinions” and “attitudes” of research subjects. 

Key premises in this approach sit uncomfortably with a WPR way of thinking. The presumption of sovereign subjects with “attitudes” is put in question in a WPR approach to subjectivity (Bacchi and Goodwin 2016: 49-53). Indeed, WPR treats “psychology” as a governing knowledge and a contingent historical creation that needs “to be interrogated rather than enshrined as ‘truth’” (Bacchi and Goodwin 2016: 5).

The tensions between WPR and RTA also appear in relation to the kinds of material used for an analysis. In RTA and other forms of thematic analysis, the scripts from interviews and focus groups commonly form the basis of the analysis (Byrne 2022). In WPR interviews are not a straightforward source of “opinions”. Rather, interview texts are subjected to a style of questioning that looks to identify the presuppositions underlying “statements” (Bacchi and Bonham 2016). 

In other words, RTA and WPR rely on contrasting theoretical premises which shape the questions they ask. These contrasting premises make it difficult to imagine the use of RTA to identify “themes” in a study wishing to use WPR. 

“Coding”, “topics” and “themes”

In my paper introducing WPR to music education I discovered the usefulness of “topics” as opposed to “themes” (Bacchi 2023). In preparation for the presentation, I engaged with and read the critical literature in the field. This immersion alerted me to the areas of controversy under discussion by those involved in music education. Using WPR I wanted to illustrate how it was possible to recognize “proposals” and work backwards to identify the problem representations implicit within them. I chose “proposals” within the National Plan for Music Education 2022 that aligned with the areas of controversy I had identified: progression/development, inclusion and diversity, talent and creativity, teacher training and professionalism, and evidence-based policy. I mistakenly called these areas of controversy “themes” whereas they were (simply) topics.

Is anything gained from identifying topics, as I did in the music education paper? I would see this approach as useful in a “first run” through the material, a way of indicating that the selected text (National Plan for Music Education 2022) included many proposals for change in music education programs. I noted that, in the Plan, the word “should” was used some 250 times, illustrating the large number of proposals in this highly prescriptive text (Bacchi 2023: 6). 

My decision to highlight five “topic areas” allowed me to introduce WPR thinking and to encourage a more critical questioning of the Plan. I believe listing topic areas proved a useful strategy for these purposes. Hence, it is completely possible that, in your own work, identifying “topics” may contribute to your analysis. I would stress, however, that these topics are not themes, and that they did not require coding. 

“Themes” within problem representations? 

Forms of analysis depend, in the first and last instance, on research questions. Moreover, as argued above, the sorts of research questions associated with WPR are different in kind and scope from the sorts of research questions guiding thematic analysis. A WPR analysis targets governmental problematisations. It would be inappropriate, therefore, to try to identify “themes” prior to seeking out and interrogating those problematisations. 

However, might it be possible to use coding and theme identification to organizeidentified problematizations/problem representations? If you start your analysis with the WPR questions to identify problem representations, could some form of coding be useful in their analysis? Is the counting of word usage – e.g., the 250 mentions of the word “should” – in effect, a form of coding or thematic analysis? Given that there are often plural problem representations in any practical text and allied texts, might it be useful to assign different problem representations a code? 

I invite readers to send me examples where such an analysis has been performed. The example I wish to offer applied a conventional WPR analysis despite the large amount of material the study involved. Identified problem representations were then organized into categories, as outlined below, around “themes”. Does this case-study illustrate a way in which thematic analysis may be compatible with WPR? 

Suicide prevention initiatives targeting Sámi in Nordic countries 

A 2021 article by Jon Petter Stoor et al. set out to map and examine suicide prevention initiatives among Sámi. Seventeen initiatives targeting Sámi were identified during 2005–2019, including nine in Sweden, five in Norway, one in Finland and two international initiatives. Applying the WPR questions, the authors identified “40 problematizations regarding how to prevent suicide among Sámi” (Stoor et al. 2021: Abstract). 

The authors started their analysis from “proposals” in the selected texts and worked backwards to identify problem representations. Given the large number of identified problem representations (40), they introduced five categories to organize the material in a meaningful way: “pertaining to shortcomings on individual (5), relational (15), community/cultural (3), societal (14) and health systems levels (3)”. These categories can be described as themes (rather than topics) since they capture “some level of patterned response or meaning” (Braun and Clarke 2021: 341). Moreover, it is possible to imagine how some form of coding might have facilitated the organizing of the material into these categories/themes.

Importantly, Stoor et al. (2021) did not stop the analysis there. They produced a Supplementary Table listing the 40 problem representations that had been “read off” from identified proposals. 

I offer a sample of the identified problem representations to indicate the richness of the material.

Supplementary table 3. Problematizations, category and level of intervention suggested, yielded through applying the “What is the problem represented to be?”-approach on suicide prevention initiatives targeting Sámi in Norway, Sweden and Finland. 

Problematizations:  

Young Sámi men do not have enough tools for emotional regulation 

Sámi (and non-Sámi) youth at risk of suicidality do not have an active enough lifestyle 

Sámi (and non-Sámi) young kids do not have enough coping skills to deal with life’s challenges, conflicts and mental health issues 

Young reindeer herders do not have good enough skills to take care of themselves/increase mental well-being 

Young male reindeer herders do not have good enough conflict management skills 

Sámi youth do not have enough access to peer-support 

Young male reindeer herders do not have enough access to peer support 

Young reindeer herders do not have enough access to peer support 

Young Sámi men do not have enough access to peer support 

The Supplementary Material Table conveys something of the complexity involved in thinking through how suicide prevention among the Sami was problematized. Making this amount of detail available renders it possible to raise questions about the five categories produced by Stoor et al. Does the sample of problem representaions (above) suggest another schema for organisation, perhaps by age categories? The point is that, by listing the 40 problem representations, other ways to envision the “problem” could be extracted from the material. In this way, the authors protect against the dangers of simplification that can accompany coding and thematic analysis. 

Conclusion

To summarize, research questions shape an analysis. WPR and RTA consist of theoretically distinct research questions. Hence, it is inappropriate to seek out RTA-style themes prior to applying the WPR questions. it may on occasion suit your purposes to single out different topics within a body of material, as I did in the music education paper. This analytic intervention is not, however, a thematic analysis. 

Once (plural) problem representations have been identified, it may be possible to use themes to organize them. The Stoor et al. (2021) example illustrates how problem representations can be organized by themes. Importantly, the authors developed their framework of five categories (themes) only after they had applied the WPR questions. They applied WPR thinking as a first stage in the analysis to identify problem representations and then considered how to make this material more meaningful by categorizing the different approaches to suicide prevention. While such categorization may be useful, it is important to find ways to qualify such modes of simplification. Stoor et al. use Supplementary Material to this end. 

I see the possibility of this form of thematic analysis and even the use of coding to help organize large amounts of research material in the manner applied here. I stress, however, the need to acknowledge that any imposed categorisation could be otherwise and to provide, to the best of one’s ability, the full richness of the identified problem representations. The goal in a WPR analysis is to find ways to highlight the complexity of the heterogeneous factors involved in making lives and worlds and to resist the temptation to simplify for the sake of academic conventions. 

HAVE A HAPPY HOLIDAY SEASON!

REFERENCES

Alvesson, M., & Sandberg, J. 2013. Constructing research questions: Doing interesting research. London: Sage.

Bacchi, C. 2009. Analysing Policy: What’s the Problem Represented to be? Frenchs Forest: Pearson Education.

Bacchi, C. 2023. Bringing a “What’s the problem represented to be?” approach to music education: A National Plan for Music Education 2022. Music Education Research, DOI: 10.1080/14613808.2023.2223220 

Bacchi, C. and Bonhan, J. 2016. Poststructural Interview Analysis: Politicizing “personhood”. In C. Bacch and S. Goodwin, Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan. pp. 113-122. 

Bacchi, C. and Goodwin, S. 2016. Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan. 

Braun, V. & Clarke, V. 2006. Using thematic analysis in psychology, Qualitative Research in Psychology, 3:2, 77-101, DOI: 10.1191/1478088706qp063oa 

Braun, V. & Clarke, V. 2021. One size fits all? What counts as quality practice in (reflexive) thematic analysis?, Qualitative Research in Psychology, 18:3, 328-352, DOI: 10.1080/14780887.2020.1769238 

Byrne, D. 2022. A worked example of Braun and Clarke’s approach to reflexive thematic analysis. Quality & Quantity, 56: 1391-1412. 

St. Pierre, E. A. 2019. Post qualitative inquiry, the refusal of method, and the risk of the new. Qualitative Inquiry. online first doi: 10.1177/1077800419863005.

Stoor, J. P. A, Eriksen, H. A. and Silviden, A. C. 2021. Making suicide prevention initiatives targeting Sámi in Nordic countries. BMC Public Health, 21:2035. https://doi.org/10.1186/s12889-021-12111-x  

Taylor, G.W. and Ussher, J.M. 2001. Making sense of S&M: a discourse analytic account. Sexualities 4, 293/314 

As part of an ongoing project mounted by the Georg Eckert Institute, I have put together some thoughts on the various ways in which WPR might be useful in studying current developments in relation to digitalization in education. In the previous Research Hub entry on “computational thinking” (CT) we touched briefly on the growing proliferation of computer science courses and programs in CT at all levels of K-12 education in many countries. Wing (2010: 4-5) produces a long list of courses and programs in CT in American and UK professional organizations, government, academia, and industry. This growth, as noted previously, is not replicated in the Global South (Belmar 2022). 

The topic of digitalization in education extends well beyond the discussions of CT. As will be seen in this entry, we can consider such innovations as facial recognition in educational settings and modes of electronic assessment in international programs, described by Biesta (2019) as the “global education measurement industry” (GEMI), as part of this topic. This entry suggests how WPR can be a useful tool in exploring this increasingly important dimension of education practices. It makes links to recent research articles that provide valuable insights into the sorts of questions that need to be asked and the range of methods that can be marshalled to assist in this task.

To guide this discussion I identify five ways, involving WPR, to produce useful analysis of developments in digitalized education practices: 

1. Consider the general proposition that the “problem” is represented to be lack of digitalization in education.
2. Focus on particular policy statements that clarify this proposition – working backwards to identify implicit problem representations.
3. Among the statements taken as starting points (proposals) for this analysis, include “visionary” statements (sociotechnical imaginaries). 
4. Focus on specific governmental mechanisms and how they operate. 
5. Focus on academic debates. 

Let us take each of these in turn. For all five suggested modes of intervention, I provide brief comments on the theoretical precepts at work. 

Consider the general proposition that the “problem” is represented to be lack of digitalization in education.

Elsewhere ( KEYNOTE ADDRESS – CAROL BACCHI – 18 August 2022) I have described how the WPR analytic strategy can be used to identify the ways in which policy “problems”, and other “problems”, are produced as particular sorts of problems. I have suggested starting from what I call “proposals” (or proposed solutions) and “working backwards” to see how the “problem” is represented within them. The argument here is that what is proposed as an intervention reveals a target for change and hence what is produced/represented as problematic, as “the problem”. For example, a policy that introduces an activity regime for children as a response to so-called “childhood obesity” produces the “problem” as children’s lack of activity. That is, children’s “obesity” is problematized in terms of children’s lack of activity. This problematization becomes what I call a “problem representation”. It provides the starting point for the remaining WPR questions that target underlying presuppositions, genealogy and effects (see Alexander & Coveney 2013; Alexander et al. 2014).

Applied to the topic of digitalization and education, one could start with the proposition that, if digitalization (in its many guises) is put forward as the proposal/proposed solution, it follows that lack of digitalization is produced as the “problem”. Does this reframing advance the analysis in any way? It assists subsequent stages of critical analysis by providing a focal point for further interrogation. This further interrogation takes us to the WPR list of questions (Bacchi and Goodwin 2016: 20). 

Question 2 is pivotal. It reads: “What deep-seated presuppositions or assumptions underlie this representation of the “problem” (problem representation)”. Basically, the point of this question is to probe how the concept “digitalization” is conceptualized since this will affect what is “done” in its name. Another way to put this point is to ask what meanings need to be in place for this concept to be intelligible. Identifying such meanings opens the concept to critical reflection on its operation in policy and other programs. Digitalization does not have a clear and obvious meaning. It depends for its meaning on a specific conception of knowledge, on notions of technological “progress”, on conceptions of human capacities (link to CT in previous blog). 

Clearly proposals in favour of digitalization in education are tied to certain problematizations of education – an example of what I call “nesting”. “Nesting” refers to the ways in which problem representations “nest” or reside within one another, requiring the use of the WPR questions several times in the one analysis. In the case in hand, approaches to digitalization in education will be linked to conceptions of education as either an instrumental tool for career advancement or, by contrast, as an emancipatory practice. Question 2 provides the space to consider how these competing perspectives on education get played out in the field of digitalization (see Hanell 2018). 

As part of a WPR analysis, there would also be a need to take a “long view” of the digitalization debate (Question 3). That is, it is necessary to consider contesting positions on the role of technology in general, and over time.

Subsequent questions would prompt reflections on alternative problematizations (Q4), the effects of focusing on digitalization as a key to educational development (Q5), the specific practices involved in supporting this position (Q6) and how one’s own proposals problematize the issue (Step 7).

• Use specific policy proposals to elaborate the kind of “problem” lack of digitalization is represented to be. 

To proceed with our analysis, it is necessary to identify proposals or proposed solutions in the selected texts or other forms of site (e.g. buildings, maps, etc.). This analytic strategy is linked to Foucault’s recommendation that critical analysis start from “practical texts” or “prescriptive texts” that provide guides to conduct (Bacchi 2009: 34). Building on this recommendation, WPR postulates that any proposal for change signals what needs to change and hence what is represented and produced as problematic (as “the problem”). Proposals for change therefore allow us to identify problematizations or problem representations. 

What do “proposals” look like? They could appear as “aims” or “objectives” in the selected texts/material. They could, as we shall see shortly, appear as visionary statements about the future – proposals about all the “good things” that will follow digitalization. They are sometimes more oblique and difficult to recognize. For example, a general statement about digitalization increasing employability (i.e. lacking any specific directions for change) is still a form of proposal in which people’s lack of employability is represented to be a “problem”. 

I offer Etienne Woo’s paper on China’s World Class University (WCU) policy to illustrate this thinking at work. Woo uses extracts from the WCU Plan 2015 as proposals that prompt a WPR analysis. The WCU Plan is put forward as a “practical” or “prescriptive” text. The “prescriptions” read:

The most prominent problem appears to be that an insufficient number of WCUs and world-class disciplines within universities is holding back on “China’s core national competitiveness” and the “foundation of long-term development” and impeding the “historical leap from a big country of higher education to a country of powerful higher education” (WCU Plan 2015, Introduction).

Woo proceeds to apply the WPR questions to these proposals, producing an insightful analysis (see also Hoydal et al. 2021). 

Another article by Zhou et al. (2022), on the internationalization of China’s higher education policy, adopts a mode of WPR analysis described as an “integrated analysis”. It follows the form of application adopted in the last five chapters of Analysing Policy (Bacchi 2009) where the WPR questions are not explicitly stated but operate in the background of the analysis. To signal their relevance at certain points, I use the notation Q1, Q2 etc. Zhou et al. adopt this convention, which is actually unnecessary. I introduced the notation system to illustrate an analytic point. I apologize if readers thought I meant for them to apply the notations. In a Research Hub entry on 31 July 2021, I offer Larsson’s (2021) article as a useful example of how to use WPR in an integrated analysis without adopting the notation. Larsson illustrates how it is possible to perform a WPR analysis without listing the WPR questions (i.e. they operate in the background). 

Regardless of whether one adopts a style of analysis that applies the WPR questions sequentially or an “integrated analysis”, starting one’s analysis for explicitly stated proposals works best to identify problem representations. 

• Starting from visionary statements/proposals.

I have decided to include visionary statements, sometimes referred to as “sociotechnical imaginaries”, as a form of proposal, with the proviso that “ideas” are not seen as drivers of change. Lina Rahm (2021) makes a strong case for the usefulness of “imaginaries” as focal points for WPR-style analyses. A special Research Hub entry (29 Nov 2022), entitled, “Sociotechnical imaginaries and WPR: Exploring connections”, canvases some of the theoretical points that deserve attention should a researcher decide to proceed down this path. This extract from the WCU Plan 2015 signals this potentially fruitful analytical target: “historical leap from a big country of higher education to a country of powerful higher education” (WCU Plan 2015, Introduction in Woo 2022).

• Focus on specific governmental mechanisms and how they operate

To study governing practices in the broad sense associated with governmentality (Bacchi and Goodwin 2016: 42) attention is directed to “rationalities” and “technologies” in both conventional political institutions and in the multiple agencies and groups (academics, professionals, experts) which contribute to societal administration. Governmental rationalities are the rationales produced to justify particular modes of rule, to make “some form of that activity thinkable and practicable both to its practitioners and to those upon whom it was practiced” (Gordon 1991: 3). “Technologies” encompass the mechanisms through which governing takes place, including specific instruments such as censuses, league tables, performance data, and case management, and the vast array of programs and policies produced to shape the conduct of individuals and groups. 

I have selected three mechanisms relevant to the topic of digitalization in education, indicating how each can be analysed using WPR. The three mechanisms include: “learning to code”, facial recognition and international forms of assessment/evaluation. 

Williamson (2016) targets the idea of “learning to code” as part of a major reform agenda in education policy in England. His analysis draws upon a governmentality perspective to highlight the influence of “networks between governmental, civil society and commercial actors”. A particular focus is the subjectification effects of proposals to include “learning to code” in K-12 curricula (Question 5 in WPR). According to Williamson, through such programs, CT shapes students’ digital subjectivities, preparing them as: 

“the ideal participants for the ‘digital governance’ of the reluctant state, as citizens with the technical skills, computational thinking and solutionist mindsets to ‘hack’ solutions to problems of contemporary governance on behalf of the government.” (Williamson 2016: 54)

Andrejevic and Selwyn (2019) produce a useful analysis of the introduction of facial recognition of students as a governing mechanism. They raise the sorts of questions that a WPR analysis would encourage. For example, they elaborate the model of learning that accompanies facial recognition technologies, a model of learning that marginalises issues of social context. They argue that “learning is reduced to a set of psychological traits and characteristics that are discernible through the face, and are open to manipulation” (Andrejevic and Selwyn 2019: 7). Emphasizing the importance of resistance practices, they draw on de Certeau (1984) to consider the deployment of improvised and opportunistic “tactics”. On the topic of resistance (Question 6 in WPR), Magilchrist draws attention to “marginal subject figures” who “create diffraction patterns, illustrating how the world can look otherwise” (2018: 8; see also 2019). 

As a third mechanism I have chosen forms of assessment of students and how these modes of assessment produce “problems” as particular sorts of problems. My target is the Programme for International Student Assessment (PISA), introduced by the OECD (see Bacchi 2020). The kinds of questions WPR brings to the topic include: What sorts of things are tested for? What assumptions about “knowledge” and “skills” underpin the testing regimes? What implications follow for students and education more generally? Grek (2014) points out that what can be measured becomes what is tested for, shaping educational outcomes. Thoutenhoofd (2018) notes that learning becomes what the data are making visible, that is, specific tasks that become measurable outcomes, dubbed “learnification” by Biesta (2015).

• Focus on academic debates

WPR can be used to analyse the theoretical literature on digitalization and education. In Analysing Policy (2009: 249) I explain that all theories are forms of proposal and therefore contain problem representations. Hence, they can be subjected, productively, to the questions in the “What’s the Problem Represented to be?” approach. I follow up this suggestion in my analyses of health policy (pp. 128-136), criminal justice policy (pp. 103-105) and gambling policy (pp. 249-251). 

Other authors have pursued the suggestion that WPR can be applied to forms of academic text and argument. See for example Månsson, J., & Ekendahl, M. (2015). In an article on contrasting approaches to the notion of critique or criticality, Primdahl et al. (2018) undertake an analysis of the “content of the argumentation” in terms of problematizations, in effect applying WPR to the selected articles. In an example relevant to the topic of digitalization and education, Puukko (2024) uses WPR to study problem representations in the academic accounts of mobilizations. As Backman and Lofstrand (2022: 273) argue, “every piece of published research is in a sense a ‘prescriptive text’”. This insight means that every piece of published research is a form of proposal, with implicit problem representations. It therefore becomes possible to use WPR to produce interesting literature reviews. 

My goal in this entry has been to provide some guidance on how to identify projects in the field of digitalization and education that invite a WPR analysis. I have no doubt that there are many others and would welcome you sharing them with me or with the WPR list. My hope is that some of you may feel inspired to pursue one of these options. It is a topic that demands attention. 

References

Alexander, S. A. & Coveney, J. 2013. A critical discourse analysis of Canadian and Australian public health recommendations promoting physical activity to children. Health Sociology Review, 22(4): 353-364.  

Alexander, S. A., Frohlich, K. L. and Fusco, C. 2014. ‘Active play may be lots of fun, but it’s certainly not frivolous’: the emergence of active play as a health practice in Canadian public health. Sociology of Health & Illness, 36(8): 1188-1204.

Andrejevic, M. & Selwyn, N. 2019. Facial recognition technology in schools: critical questions and concerns, Learning, Media and Technology, DOI: 10.1080/17439884.2020.1686014 

Bacchi, C. 2009. Analysing Policy: What’s the Problem Represented to be?  Frenchs Forest: Pearson Education. 

Bacchi, C. and Goodwin, S. 2016. Poststructural Policy Analysis: A guide to practice. NY: Palgrave Macmillan.

Backman, C. and Löfstrand, C. H. 2022. Representations of Policing Problems and Body-Worn Cameras in Existing Research. International Criminal Justice Review, 32(3): 270-290. 

Belmar, H. 2022. Review on the teaching of programming and computational thinking in the world. Frontiers in Computer Science, DOI 10.3389/fcomp.2022.997222 

Biesta, G. 2015. What is Education for? On Good Education, Teacher Judgement, and Educational Professionalism. European Journal of Education, 50(1). DOI: 10.1111/ejed.12109 

Biesta, G. 2019. “What Kind of Society Does the School Need? Redefining the Democratic Work of Education in Impatient Times.” Studies in Philosophy and Education 38 (6): 657–668. doi:10.1007/s11217-019-09675-y. 

de Certeau, M. 1984. The Practice of Everyday Life. Translated by Steven Rendall. Berkeley: University of California Press. 

Gordon, C. 1991. Governmental rationality: An introduction. In G. Burchell, C. Gordon, P. Miller (Eds.), The Foucault effect: Studies in governmentality. Chicago: University of Chicago Press.

Grek, S. 2014. OECD as a site of coproduction: European education governance and the new politics of ‘policy mobilization’, Critical Policy Studies, 8:3, 266-281, DOI: 10.1080/19460171.2013.862503 

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https://doi.org/10.1007/s11217-017-9584-1

Williamson, B. 2016. Political computational thinking: policy networks, digital governance and ‘learning to code’, Critical Policy Studies, 10:1, 39-58, DOI: 10.1080/19460171.2015.1052003 

Wing, J. 2006. Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10. 

1145/1118178.1118215.

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In the last Research Hub entry (29 Sept 2023) I experimented with ChatGPT, asking targeted WPR questions. I concluded that the chatbot did not perform well in terms of understanding the theory underpinning WPR and that it could only summarize existing material and that to which it was explicitly directed. None of this should come as a surprise. As King (2023: 105) relates, as long ago as the 1830s, Ada Lovelace explained that Charles Babbage’s Analytic Engine – widely regarded as the world’s first computer – “has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform”. 

While this limitation may be widely recognized, in another development, the benefits and learning outcomes associated with using computers are increasingly lauded. The term “Computational Thinking” (CT) is becoming prevalent in educational curricula and in associated testing regimes, as a shorthand for those benefits. PISA’s 2021 ICT Framework (OECD 2019: 3.2.2 para 63) recognizes “computational thinking” as an ICT competency within the mathematics curriculum:

         “In this context, computational thinking refers to formulating problems and designing their solutions in a form that can be executed by or with a computer (Cuny, Snyder and Wing 2010).” (OECD 2019: 6.1.1 para 168; emphasis added). 

The literature on CT (Computational Thinking) is voluminous and repeats some common themes: first, that CT refers to the thought processes stimulated by working with computers; and second, that those thought processes represent a valuable way of thinking that students need to absorb due to our increasingly digitalized world.

In this entry I raise questions about CT as a recommended thinking style, emphasizing how it relies upon and operates through a problem-solving analytic that presumes “problems” as pre-existing entities.  The argument proceeds through several stages. First, I present common claims concerning the educational benefits of CT. Next, I raise some of the useful criticisms that have been developed. Finally, I make the case that CT operates within a problem-solving framework that sets the terms of reference for its operation, a framework that imposes severe limitations on CT as a way of thinking.

 What CT claims to offer

Lodi and Martini (2021) offer a useful introduction to the history of “computational thinking” (see also Tedre and Denning 2016). They trace the emergence of the field of computing as an autonomous discipline to the end of the 1950s and the early 1960s. The contributions of two researchers, Seymour Papert and Jeannette Wing, to current thinking on the topic are highlighted.  

The expression “Computational Thinking” (CT) was used for the first time by Papert in 1980 (Lodi and Martini 2021: 884). For Papert, CT is the result of his constructivistapproach to education, “where social and affective dimensions are as important as the technical content” (Lodi and Martini 2021: 884). Wing (2006) revived the term CT in a much-quoted 2006 paper. For Wing CT is the result of teaching Computer Science. She (2010) makes the case that learning to think like a computer scientist would be a benefit for everyone, in whatever profession involved. 

Following Wing’s intervention, initiatives to bring computer science into all levels of K-12 education were introduced in many countries. Wing (2010: 4-5) produces a long list of courses and programs in CT in American and UK professional organizations, government, academia, and industry. This growth, it should be noted, is not replicated in the Global South (Belmar 2022). 

Proposals to teach CT emphasize that training in computer science produces a cluster of “skills” or “mental strategies” “useful to solve problems” (Lodi and Martini 2021: 896). The most commonly listed “skills” include: abstraction, decomposition, pattern generalisation, debugging and algorithm design. There is an occasional mention of “critical thinking”, though what is meant by the term is not elaborated (Shute et al. 2017). 

In the growth and proliferation of interest in CT a major issue concerns whether or not the “mental strategies” associated with computer use automatically transfer to other disciplines. According to Lodi and Martini (2021: 884; emphasis in original), both Papert’s and Wing’s CT “come (or seem to come) with the idea that the competencies acquired as CT will easily (or even automatically) transfer to other disciplines”. And, they argue, this “largely unverified” assertion about the transferability of intellectual skills, in both Papert and Wing, helps to explain the wide appeal of CT (Lodi and Martini 2021: 884).  

It is important to recognize the distinctiveness of Papert’s (1980) understanding of CT. As Lodi and Martini (2021: 900) explain, transfer, in Papert, “is something that does not happen automatically, and needs an active and deliberate effort”. Links can be drawn between this 1980s iteration of CT and a “student-friendly” version of problem-solving, associated with John Dewey and with cognitive constructivism (Bacchi 2020). For Papert, “only a social and affective involvement of the student in constructing a (computational) artifact will make programming an interdisciplinary tool for learning (also) other disciplines” (Lodi and Martini 202: 883). Wing (2010: 5), by contrast, makes a straightforward claim that “The educational benefits of being able to think computationally transfer to any domain by enhancing and reinforcing intellectual skills”.

Among those intellectual skills that will (it is argued) be transferred, Wing emphasizes the importance of abstraction. According to Wing (2010: 1), the “abstraction process” is the “most important and high-level thought process in computational thinking”. In abstraction the focus is only on the important information, ignoring irrelevant details: “It is used to capture essential properties common to a set of objects while hiding irrelevant distinctions among them”. 

The OECD’s PILA (Platform for Innovative Learning Assessments) Computational Problem Solving Module specifies the “many levels of abstraction” that CT includes: 

“Using computation to solve problems requires the ability to think in a certain way, which is often referred to as ‘computational thinking’. The term originally referred to the capacity to formulate problems as a defined set of inputs (or rules) producing a defined set of outputs. Today, computational thinking has been expanded to include thinking with many levels of abstraction (e.g., reducing complexity by removing unnecessary information), simplifying problems by decomposing them into parts and identifying repeated patterns, and examining how well a solution scales across problems.”  (OECD 2021; emphasis added)

The importance of training students to develop skills in computational problem-solving is sheeted home to the demands of the labour market:

“Computers and the technologies they enable play an increasingly central role in jobs and everyday life. Being able to use computers to solve problems is thus an important competence for students to develop in order to thrive in today’s digital world” (OECD 2021; emphasis added) 

The need to be able to use computers is not in dispute. The suggestion that problems ought to be formulated in ways computers can solve and that this task involves simplification and decomposition, however, raises issues that need to be considered.

What critics bring to our attention

It is difficult to locate critical analyses of CT. The vast majority of articles express enthusiasm about the development of computer skills and “computational thinking”.  For example, Ogegbo and Ramnarain (2022: 203) state that “CT is acclaimed as an approach to teaching the essential 21^st-century skill set that every student requires to thrive in the changing world”. Belmar (2022: 1) describes computational thinking as “the fundamental skill for scientific, technological, and economic development for the twenty-first century”. He acknowledges, however, that “this diagnosis that is unveiled in rich countries remains nebulous for poor countries”.

Kafai et al. (2020) identify three theoretical approaches to CT: cognitive CT, situated CT and critical CT. Critical CT “proposes an analytical approach to the values, practices, and infrastructure underlying computation”, reminding us that “computing is not an unequivocal social good”. In his thought-provoking commentary on technological/digital innovation King (2023: 105, 108) expresses concern about the way in which computational thinking comes to mean (simply) the sorting of “information”, and how a “computational theory of mind” overlooks the contextual and the social (King 2023: 105, 108).

Williamson’s (2016: 42) work on the place of “learning to code” in the UK K-12 curriculum draws upon a governmentality perspective to highlight the influence of “networks between governmental, civil society and commercial actors”. A particular focus is the subjectification effects of proposals to include “learning to code” in K-12 curricula. Says Williamson, through such programs, CT shapes students’ digital subjectivities, preparing them as: 

“the ideal participants for the ‘digital governance’ of the reluctant state, as citizens with the technical skills, computational thinking and solutionist mindsets to ‘hack’ solutions to problems of contemporary governance on behalf of the government.” (Williamson 2016: 54)

Importantly Williamson describes CT as a “way of thinking” or a “style of thinking” that extends beyond the classroom. He characterizes this way of thinking as politicalcomputational thinking: 

“Political computational thinking is a style of thought, then, that aims to translate social phenomena into computational models that can then be solved by being formalized as step-by-step algorithmic procedures that can be computed as proxies for human judgment or action.” (Williamson 2016: 40; emphasis added)

This general style of governing “takes technological change as the model for political innovation” (Barry 2001: 2 in Williamson 2016: 40). 

In Williamson’s account, the downsides of approaching governing through CT, with its simplifying practices of abstraction and decontextualization, are stark. The focus shifts from people’s lives to things that can be measured and tabulated. As one contemporary example illustrating this point, in the recent robo-debt tragedy in Australia, the decision to use income averaging to identify those with welfare debts ignored the fact that the targeted population frequently did not work standard full-time hours (Whiteford 2023). As Williamson (2016: 54) concludes:

“This emerging solutionist state is one in which political computational thinking, based on a technocratic logic that all social phenomena can be formalized into computable models, has become the main governmental style of thought”.

The missing link: problem-solving

To understand what is going on with CT both as an educational innovation and as a style of governmental thinking requires attention to the underlying premise of these approaches: that living and governing rely on problem-solving. Even our most astute critics of CT appear to bypass the way in which a CT style of thinking and governing starts from the premise that there are problems, pure and simple, that need to be solved. Despite his careful reflections on the constitutive role that technology plays in human affairs, King (2023: 63, 104) is happy to claim that human beings are “problem-solving creatures, after all”.

Williamson highlights the importance of thinking about “styles of thought” in governing. He elaborates that “styles of thinking” can be characterized as “particular ways of thinking, practicing, making explanations, conceptualizing problems and proposing solutions in a given professional or disciplinary community” (Williamson 2016: 40; emphasis added). I have italicized the words “conceptualizing problems” because it is this aspect of CT that, in my view, goes underexamined.

The literature on CT tends to portray its role as a “problem-solving approach” as simple common-sense. The intrinsic connection between CT and problem-solving appears in the definition offered at the beginning of this entry and repeated here: “In this context, computational thinking refers to formulating problems and designing their solutions in a form that can be executed by or with a computer (Cuny, Snyder and Wing 2010).” (OECD 2019: 6.1.1 para 168; emphasis added). Even Papert, who ought to be acknowledged as pioneering in his questioning of the ways in which computers are conventionally used for drill and practice, still worked within a problem-solving mindset. With Faurzeig, he (2011: 490) describes how the “activity of programming” “fosters an experimental approach to solving problems”. In the dominant iteration, associated with Wing (2010: 1), computational thinking “describes the mental activity in formulating a problem to admit a computational solution”.

On the one hand the reference to “formulating problems” appears to endorse a more open-ended meaning of “problem” – since “problems” can be made (formulated). However, the fact that formulation depends on a single criterion, to make the “problem” amenable to computer analysis, imposes severe constraints on what is considered to be relevant. No space exists to interrogate “problems” in terms of underlying presuppositions, genealogy or effects (Bacchi 2009).

Moreover, the “five basic concepts” needed to produce computational thinking as a “viable problem-solving approach” all refer to problems simply as existing states or conditions:

• Decomposition – This involves breaking down a complex task into smaller, and more manageable components [commonly described as “problem decomposition”, Grover and Pea 2013: 2); 
• Recognition of pattern – This involves identifying and defining trends or patterns within a problem; 

• Abstraction, which involves identification of particular similarities and differences between comparable problems to work towards a solution; 
• Algorithm design, which involves the development of step-by-step guidelines for solving a problem and can be used again to answer similar problems; and 
•  Automation, which involves the use of technological tools to mechanise problem solutions. (Ogegbo and Ramnarain 2022: 204-205).

I have italized terms in this description that indicate how each designated CT “concept” relates to and depends upon a conception of “problems” as pre-existent and waiting to be addressed/solved.

Some might want to make the case that the emphasis in CT on mathematical-type problems means that analyses of their underlying presuppositions, genealogy and effects, are simply irrelevant because (after all) mathematical problems are pretty clear-cut. However, even within mathematics, there is dispute about the very nature of problem-solving and the meaning of “problems” (Bacchi 2020: 85). Moreover, Wing (2010: 1; emphasis added) argues that CT targets not just “mathematically well-defined problems” but also “real-world problems”: 

“When I use the term computational thinking, my interpretation of the words “problem” and “solution” is broad; in particular, I mean not just mathematically well-defined problems whose solutions are completely analysable, e.g., a proof, an algorithm, or a program, but also real-world problems whose solutions might be in the form of large, complex software systems. Thus, computational thinking overlaps with logical thinking and systems thinking.”

It follows that, despite the emphasis on problem formulation, in CT, problems, whether mathematical or “real-world”, are conceptualized as pre-existing states or conditions. They exist out there somewhere in the world to be found and decomposed “into as many parts as may be necessary for finding the solution” (Derrida 1967/1978: 287 in St Pierre and Jackson 2014: 715). 

None of this is really surprising given the ubiquity of the problem-solving paradigm, which dominates the intellectual and policy landscape. Elsewhere (Bacchi 2020) I describe the prevalence of problem-solving knowledge across a wide array of disciplines and in everyday usage in contemporary western societies and international organizations. Problem-solving knowledge is used in the French sense of savoir as a form of background knowledge or “truth” discourse. I describe how the presumption of pre-existing “problems” in problem-solving knowledge removes from consideration how “problems” are produced and represented, limiting political debate and regulating political subjects.

Usefully, Williamson (2016: 54) alerts us to the ways in which political computational thinking produces a “solutionist mindset” and a “solutionist state” – “a technocratic logic that all social phenomena can be formalized into computable models”. Troeger et al. (2023: 95) note that the software development process is often subject to a problem-solving paradigm. The literature on software development and design assumes that there “are” problems that can be identified and understood.

To step outside a “solutionist mindset” requires challenging the presumption of pre-existing problems. To this end I recommend bringing WPR into the picture – into classrooms, into board rooms, into parliamentary committees. WPR postulates a contrasting and contesting “style of thinking”. It alerts us to the depoliticizing effects produced by treating “problems” as if they simply exist waiting to be solved. To think critically about CT requires challenging the problem-solving knowledge it relies upon and promotes. Perish the thought that we should all end up “thinking” like ChatGPT!  

[Guide to usage:  Readers may have noticed some unevenness in my use of quotation marks around problems. I exclude quotation marks when the term is used in relation to a perspective that accepts problems as pre-existing entities. I have also adopted the use of a hyphen in problem-solving whether it operates as a noun or as an adjective. I have done so to signal that, in its pervasiveness, problem-solving knowledge operates as an event, in the Foucauldian sense, “the interweaving of relations of power and domination” (Tamboukou, 1999: p. 207): “Events are not simply bits of data; they need to be analyzed in terms of the practices that give rise to them” (Bacchi & Goodwin, 2016: p. 46). The hyphen in “problem-solving” is excluded when the term so appears in other publications.] 

References

Bacchi, C. 2020. Problem-Solving as a Governing Knowledge: “Skills”-Testing in PISA and PIAAC. Open Journal of Political Science, 10, 82-105.

Bacchi, C. 2009. Analysing Policy: What’s the problem represented to be? Pearson Education, Frenchs Forest.

Bacchi, C. and Goodwin, S. 2016. Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan.

Barry, A. 2001. Political Machines: Governing a Technological Society. London: Athlone Press. 

Belmar, H. 2022. Review on the teaching of programming and computational thinking in the world. Frontiers in Computer Science, DOI 10.3389/fcomp.2022.997222 

Cuny, J., Snyder, L. and Wing, J. 2010. Demystifying computational thinking for non-computer scientists. https://www.cs.cmu.edu/~CompThink/resources/TheLinkWing.pdf

Derrida, J. 1978. Writing and difference (A. Bass, Trans.). Chicago, IL: The University of Chicago Press. (Original work published 1967)

Feurzeig, W. and Papert, S. A. 2011. Programming-languages as a conceptual framework for teaching mathematics. Interactive Learning Environments, 19(5): 487-501.  

Grover, S. and Pea, R. D. 2013. Computational Thinking in K-12: A Review of the State of the Field. Educational Researcher, 1-6. DOI: 10.3102/0013189X12463051

Kafai, Y. B., Proctor, C., & Lui, D. 2020. From theory bias to theory dialogue: Embracing cognitive, situated, 

and critical framings of computational thinking in K-12 CS education. ACM Inroads, 11(1), 44–53. https://doi.org/10.1145/3381887.

Lodi, M. and Martini, S. 2021. Computational Thinking, Between Papert and Wing. Society & Education, 30: 883-908. https://doi.org/10.1007/s11191-021-00202-5 

OECD 2019. PISA 2021 ICT Framework. OECD 2019.

OECD 2021. PILA Computational Problem Solving Module. https://pilaproject.org/guides/computational-problem-solving-framework

Ogegbo, A. A. & Ramnarain, U. 2022. A systematic review of computational thinking in science classrooms Studies in Science Education, 58(2): 203-230. DOI: 10.1080/03057267.2021.1963580 

Papert, S. 1980. Mindstorms: Children, Computers, and Powerful Ideas. NY: Basic Books.

Rose, N. 1999. Powers of Freedom: Reframing Political Thought. Cambridge: Cambridge University Press.

Shute, V. J., Sun, C. and Asbell-Clarke, J. 2017. Demystifying Computational Thinking. Educational Research Review, 22, 142-158. 
https://doi.org/10.1016/j.edurev.2017.09.003

St. Pierre, E. A. and Jackson, A. Y. 2014. Qualitative Data Analysis after Coding. Qualitative Inquiry, 20(6): 715-719.

Tamboukou, M. 1999. Writing Genealogies: An Exploration of Foucault’s Strategies for Doing Research. Discourse: Studies in the Cultural Politics of Education, 20, 201-217. https://doi.org/10.1080/0159630990200202

Tedre, M. and Denning, P. J. 2016. The Long Quest for Computational Thinking. Proceedings of the 16th Koli Calling Conference on Computing Education Research, November 24-27, 2016, Koli, Finland: pp. 120-129. 

Troeger, J., Zakharova, I., Macgilchrist, F. and Jarke, J. 2023. Digital ist besser!? – Wie Software das Verständnis von guter Schule neu definiert. In A. Bock et al. (eds) Die datafizierte Schule (The Datafied School), https://doi.org/10.1007/978-3-658-38651-1_4 

Whiteford, P. 2023. “Income averaging” lacked basic common sense. The Conversation, 16 March. 

Williamson, B. 2016. Political computational thinking: policy networks, digital governance and “learning to code”. Critical Policy Studies, 10:1, 39-58, DOI: 10.1080/19460171.2015.1052003 

Wing, J. 2006. Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10. 

1145/1118178.1118215.Wing, J. M. 2010. Computational thinking: What and why? Retrieved from http://www.cs.cmu.edu/∼CompThink/resources/TheLinkWing.pdf.

Many of us are exploring the dazzling displays of text composition offered by ChatGPT.  People I know have used it to produce mission statements and program guidelines. I became curious to see how well it might cope with questions that emerge from WPR thinking. I found some surprises, which I elaborate below. In this entry I am restricting my comments to experiments with ChatGPT-3.5.

A plethora of online and published sources are pointing to limitations and lapses in the kinds of replies ChatGPT produces. For example, ChatGPT admits a “knowledge cutoff in September 2021” (see response below). Hence, should you be looking for up-to-date material on (say) medical interventions, you need to look elsewhere.

More disturbing is the tendency for ChatGPT to make things up in replies. That is, it fabricates real-sounding information, which is certainly a concern for students, researchers and others (Botpress Community 2023). A US judge fined two New York lawyers for including five fictitious case citations in their legal brief. The firm’s statement argued that “We made a good faith mistake in failing to believe that a piece of technology could be making up cases” (Reuters in Melbourne Age, 24 June 2023, p. 26). What can I say? Believe it!

I learned about this particular ChatGPT idiosyncrasy the hard way. In the context of the three recent Research Hub entries on COVID-19 mortality rates (30 May, 29 June, 30 July 2023), I asked ChatGPT (12 June 2023): “Is it possible to specify the social and environmental causes of human mortality on the standardised international medical death certificate?” 

Here is the reply in full: “As of my knowledge cutoff in September 2021, the International Form of Medical Certificate of Cause of Death (ICD-10) does not provide specific codes to capture social and environmental causes of mortality. The ICD-10 focuses primarily on capturing medical and pathological causes of death. However, efforts have been made to expand the coding system to include these factors in the future.

To address this limitation, the World Health Organization (WHO) developed the Social Determinants of Health (SDH) Mortality Reporting Form. This form allows for the inclusion of social and environmental factors contributing to mortality. It collects information on various dimensions such as education, occupation, living conditions, and lifestyle factors.

While the SDH Mortality Reporting Form is not currently integrated into the standard International Medical Death Certificate, it provides a framework for capturing important non-medical causes of death. It serves as a valuable tool for data collection and analysis to better understand the social and environmental determinants of health outcomes.”

References (as provided with the reply):

1. World Health Organization. (2016). Social Determinants of Health Mortality Reporting Form: Implementation Guide. Retrieved from 

Click to access 9789241510109_eng.pdf

• World Health Organization. (2016). International Form of Medical Certificate of Cause of Death. Retrieved from https://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf

This reply came as quite a surprise given that I have been researching the topic for several months and was unaware of the SDH Mortality Reporting Form. Diligent researcher that I am, I pursued the matter. I asked ChatGPT: “What is the World Health Organization (WHO) Social Determinants of Health (SDH) Mortality Reporting Form? Where can I access a copy?”

The reply arrived: “As of my last knowledge update in September 2021, there was no specific World Health Organization (WHO) Social Determinants of Health (SDH) Mortality Reporting Form available.”

Indeed. Needless to say, the reference previously offered to support the claim about the existence of such a form “could not be found”. 

Importantly, I was able to check on the accuracy of the claim about the existence of a SDH Mortality Reporting Form because I had researched extensively in the field. As Ian Bogost (2022) notes, this situation creates a dilemma: “If one already needs to possess the expertise to identify the problems with LLM-generated text, but the purpose of LLM-generated texts is to obviate the need for such knowledge, then we’re in a sour pickle indeed”. 

My target for today is a related “sour pickle”. I intend to use WPR to put in question the general characterization of ChatGPT as “left-leaning” or “progressive” in its political “views”. Numerous articles set out to illustrate this “bias”. Jeremy Baum and John Villasenor (2023), from the Brookings Institute, found that “for certain combinations of issues and prompts, in our experiments ChatGPT provided consistent – and often left-leaning – answers on political social issues”. David Rozado, writing in Social Sciences (2023), concurs that ChatGPT answers manifested “a preference for left-leaning viewpoints”. The near consensus on this viewpoint so disturbed Elon Musk that he plans to build “TruthGPT”, a competitor to OpenAI’s ChatGPT, which he describes as displaying a “woke” bias (WIRED 2023). 

I intend to challenge this characterization of ChatGPT as “left-leaning”. In making this challenge, one needs to consider just what “left-leaning” implies and whether it is possible to draw such sharp left-right distinctions in political orientation. Through asking WPR questions, I discovered that replies to my questions tended to be liberal/neo-liberal in character rather than “left-leaning”, as I proceed to demonstrate. To make this case I draw on the ability of ChatGPT to hold “conversations”, making it possible to “conduct a structured dialog” and to cause ChatGPR to take a position on political issues. The targeted issues are “childhood obesity” and “Aboriginal deaths in custody”. 

I should note that ChatGPT states that it refuses to engage in “partisan political issues” with an implication of objectivity: “As an AI language model, it’s not my place to take sides or advocate for any political position” (in Gewirtz 2023). However, the reference to “political” here is narrow indeed. Should ChatGPT take its own advice seriously on this point, it would be unable to function. I had no difficulty soliciting replies to questions about “childhood obesity” and “Aboriginal deaths in custody”, leaving us to assume that these issues are not deemed to be political.

“Childhood obesity”

My opening question reads (17 June 2023): “What kind of a problem is childhood obesity represented to be in current policy responses?”

Here is the reply in full: “Childhood obesity is considered a significant problem in current policy responses. It is recognized as a public health issue requiring urgent attention due to its detrimental impact on children’s health and well-being. Policy responses aim to address the causes and consequences of childhood obesity by implementing measures such as promoting healthy eating habits, increasing physical activity, improving school food environments, and enhancing public awareness and education on the importance of a balanced lifestyle.”

Note that ChatGPT adopts a conventional reactive view of “policy responses” as “addressing” “the causes and consequences of childhood obesity” (Bacchi 2009: 1). Hence, there is no understanding of the WPR focus on problem representations, a point I return to later. The ChatGPT answer concerning “policy responses” broadly endorses liberal and neoliberal policy measures that target individual responsibility for poor health outcomes. There is a strong focus, for example, on the need for behavioural changes, such as altering eating habits and increasing physical activity, and on promoting “a balanced lifestyle”. 

I asked a follow-up question (17 June 2023): “What about links to poverty and advertising fast food?”

ChatGPR modified its response appropriately: “Childhood obesity is closely linked to poverty and the pervasive advertising of fast food. Low-income families often face financial constraints that limit access to affordable, nutritious food options, leading to a higher reliance on inexpensive, calorie-dense foods. Fast food advertising specifically targets children, creating an obesogenic environment that promotes unhealthy eating habits. Current policy responses recognize these connections and aim to address them by implementing strategies such as promoting affordable healthy food options in underserved areas, implementing restrictions on unhealthy food advertising to children, and advocating for comprehensive policies that address both poverty and the influence of marketing on children’s food choices.”

With the “challenges” provided by my supplementary question, ChatGPR moves in a more structural direction in its analysis. Poverty, for example, now gets a mention. I decided to push the issue further. 

I ask: “What about wealth redistribution?”

The reply reads: “Wealth redistribution is not typically considered a direct policy response to childhood obesity. While addressing income inequality and promoting economic equity are important societal goals, policy responses to childhood obesity tend to focus on specific interventions related to nutrition, physical activity, education, and public health. However, addressing poverty and improving access to resources, including nutritious food options and healthcare, can indirectly contribute to reducing childhood obesity rates. By addressing socioeconomic disparities, policies that aim for wealth redistribution can create an environment that supports healthier lifestyles and equitable opportunities for all children, thus indirectly impacting the prevalence of childhood obesity.” 

I got the distinct feeling from this reply that ChatGPT was “uncomfortable” with an explicit reference to the possibility of wealth redistribution, though it did its best to accommodate the argument. 

The point I would like to make from this example is that it was possible to shift ChatGPT in the direction of a more structural analysis through asking explicit follow-up questions. My concern is that the kind of “conversation” I produced here is unlikely to emerge from a casual encounter with ChatGPT. To pose follow-up questions, as I did, requires a prior engagement with the literature and with contesting points of view. As with the above example of the non-existent SDH Mortality Reporting Form, those seeking a reply would be likely to accept the initial ChatGPT response without question, reinforcing the socio-political status quo. 

“Aboriginal deaths in custody”

I asked (17 June 2023): “What kind of a problem is Aboriginal deaths in custody represented to be?”

The reply in full reads: “Aboriginal deaths in custody are widely recognized as a deeply troubling and urgent problem. These deaths represent a critical issue of human rights, social justice, and systemic racism. The overrepresentation of Aboriginal people in the criminal justice system, coupled with inadequate care and response within custodial settings, has led to a disproportionate number of deaths. This issue highlights the need for comprehensive policy reforms, including culturally appropriate support services, improved police practices, and enhanced accountability mechanisms, to address the underlying factors contributing to Aboriginal deaths in custody and ensure the protection of human rights for all individuals in custody”.

My follow-up comment reads: “You didn’t mention the living conditions of Aboriginal peoples as an underlying cause.”

Reply in full: “Apologies for the oversight. The issue of Aboriginal deaths in custody is indeed intertwined with the broader socio-economic and living conditions experienced by Aboriginal peoples. These conditions, which include higher rates of poverty, limited access to healthcare, education, and employment opportunities, along with the historical and ongoing impacts of colonization, contribute to the overrepresentation of Aboriginal individuals in the criminal justice system. Addressing the systemic issues that perpetuate these living conditions is crucial for a comprehensive approach to preventing deaths in custody and achieving meaningful change in the lives of Aboriginal peoples.”

As with “childhood obesity”, ChatGPT is quite prepared to “correct” itself when challenged to do so. Again, as with “childhood obesity”, there is a shift towards more structural explanations. 

How has this development occurred? 

I am tempted to ask: “Where have the ‘views’ in the initial replies come from?”  Here we move into the practical processes involved in generating ChatGPT. Rozado (2023) explains that “Like most LLMs [large language models], ChatGPT was trained on a very large corpus of text gathered from the Internet”. Such a corpus “would be dominated by influential institutions in Western society”. These institutions, argues Rozado, would include prestigious universities, where “the majority of professionals working in those institutions are politically left-leaning” (Rozado 2023). In addition, a team of human labelers is embedded in the training loop of ChatGPT and their “biases” might “have percolated into the model parameters” (Rozado 2023). While I dispute Rozado’s argument that the “views” he identifies are “left-leaning”, these explanations about sources of “bias” may provide insights into the pervasiveness of the liberal/neo-liberal orientation I identify in ChatGPT’s responses.

My “conversations” with ChatGPT involved application of WPR thinking. My supplementary questions (above) displayed an attempt to probe practices that are usually silenced (Question 4 in WPR; see Bacchi and Goodwin 2016: 20). Through asking these questions I was able to “force” ChatGPT to take a more structural position. However, as mentioned previously, it is possible to intervene in this fashion only if a person is already thoroughly familiar with the contesting positions around a specific issue. Generally, the first response produced by ChatGPT will be put forward in essays and articles as “accurate” and “complete”. 

I had attempted to get ChatGPT to engage with the notion of problem representations in my initial query: “What kind of a problem is childhood obesity represented to be in current policy responses?” and in the second question “What kind of a problem is Aboriginal deaths in custody represented to be?” In the last section below I consider how well ChatGPT performed in understanding and engaging with this theoretical stance. In brief, the answer is – not very well. 

ChatGPT and political theory

In reply to the first question “What kind of a problem is childhood obesity represented to be in current policy responses?” ChatGPT replied that: “Childhood obesity is recognized as a significant problem in current policy responses.” Here the term “problem” is treated as an unproblematic concept, as something that simply exists, with no consideration of how the “problem” is represented. The reply then states that “childhood obesity” is “viewed as a multifaceted health challenge”, again with no consideration of the implications of this problem representation. And, as mentioned previously, the reference to “policy responses” as “addressing” “the causes and consequences of childhood obesity” reflects a conventional view of policy processes as reactions to “problems-that-exist”. 

Similarly, in response to the question “What kind of a problem is Aboriginal deaths in custody represented to be?”, ChatGPT replies that “Aboriginal deaths in custody are widely recognized as a deeply troubling and urgent problem”. Again, “problems” are treated as pre-existent and fixed. In an attempt to respond to the wording in the initial WPR question about how the “problem” of Aboriginal deaths in custody is represented, ChatGPT has this to say about “representation”: “These deaths representa critical issue of human rights, social justice, and systemic racism (emphasis added)”. This reply shows no understanding of what is involved in problem representation (see Bacchi and Goodwin 2016).

This exchange illustrates the limitations of ChatGPR when dealing with concepts and theory. I decided to test this ability further and discovered some surprising findings.

I asked ChatGPT two direct questions about “problems” and WPR thinking. 

My first question reads (17 June 2023): “Is there a need to question how Computational Thinking conceptualises problems as pre-existing states?”

The reply reads: “Yes, there is a need to question how Computational Thinking conceptualizes problems as pre-existing states. By assuming that problems are static and predefined, we may overlook the dynamic and context-dependent nature of real-world problems. This perspective limits our ability to adapt and find creative solutions. It is crucial to explore alternative problem-solving approaches that emphasize problem formulation, reframing, and iterative problem-solving processes”.

I had not expected a “yes” reply nor the explicit questioning of “problems” as “static” and “predefined”. At the same time, there is an endorsement of the need to attend to “real-world problems” and to explore “alternative problem-solving approaches” – both positions questioned through WPR thinking. 

My second question reads (17 June 2023: I requested references): “What does it mean to say that policies constitute problems as particular sorts of problems?”

Here is the reply: “To say that policies constitute problems as particular sorts of problems means that policies define and frame problems in a specific way, shaping how they are understood and addressed. Policies determine the boundaries, scope, and parameters of a problem, as well as the preferred approaches for solving it. By defining problems through policy lenses, certain aspects may be emphasized while others are overlooked or marginalized. This framing influences the allocation of resources, the formulation of solutions, and the distribution of benefits and burdens. References:

1. Bacchi, C. (2009). Analysing policy: What’s the problem represented to be? Pearson Education Australia.
2. Howlett, M. (2011). Designing public policies: Principles and instruments. Routledge”.

This reply would satisfy many people working with WPR. However, there is slippage around the use of the term “problem”. See, for example, the references to “parameters of a problem”, and “for solving it”, as if the problem is a pre-existent entity. There is also an emphasis on problem definition, which is associated with an interpretive mode of analysis, as opposed to the focus in WPR on how “problems” are produced (or constituted) as particular sorts of problem (see Bacchi 2015: 5). While my specific questions in this case elicited some useful statements, the argument is both vague and inconsistent. 

These replies indicate two important characteristics of ChatGPT-generated text. First, ChatGPT responses reflect closely the wording in the question, as happened when I asked specifically about how “problems” are constituted as particular sorts of problem. As Baum and Villasenor (2023) note, 

“seemingly small changes in how a prompt is constructed can lead to very different responses. This is because AI-powered chatbots identify which data to draw from in a manner that is highly sensitive to the specific phrasing of the query”. 

And second, as a result of the first point, you will get what you ask for. The difficulty arises when you do not know what you ought to be asking for. ChatGPT will only reflect on what is asked of it – thinking has to precede the decision to use it! 

As my colleage Angelique Bletsas puts it, in her inimitable and insightful precis of the argument: 

“ChatGPT can summarize existing material – that which it has access to – and that to which it is directed – but that is a very different kind of activity to actual analysis. So what we understand research to involve is at stake in the move to adopt these technologies without first coming to terms with their design limitations. If you had to give a research assistant as much direction (or “prompting”) as you had to give ChatGPT, you’d quite likely be looking for a better research assistant!”

References

Bacchi, C. 2009. Analysing Policy: What’s the Problem Represented to be? Frenchs Forest: Pearson Education.

Bacchi, C. 2015. The Turn to Problematization: Political Implications of Contrasting Interpretive and Poststructural Adaptations. Open Journal of Political Science, 5: 1-12. 

Bacchi, C. and Goodwin, S. 2016. Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan. 

Baum, J. and Villasenor, J. (2023). The politics of AI: ChatGPT and political bias. Brookings, 8 May. (https://www.brookings.edu/blog/techtank/2023/05/08/the-politics-of-ai-chatgpt-and-political-bias/

Bogost, I. 2022. ChatgGPT is Dumber Than You Think. The Atlantic, 7 December.https://www.theatlantic.com/technology/archive/2022/12/chatgpt-openai-artificial-intelligence-writing-ethics/672386/

Botpress Community 2023. How Accurate is ChatGPT in Providing Information or Answers? https://botpress.com/blog/how-accurate-is-chatgpt-in-providing-information-or-answers#:~:text=As%20an%20AI%20language%20model,provide%20incorrect%20or%20inaccurate%20answers

Gewirtz, D. 2023. 6 things ChatGPT can’t do (and another 20 it refuses to do). ZD Net: https://www.zdnet.com/article/6-things-chatgpt-cant-do-and-another-20-it-refuses-to-do/

Rozado, D. 2023. The Political Biases of ChatGPT. Social Sciences 12: 148. https://doi.org/ 10.3390/socsci12030148WIRED 2023. Meet ChatGPT’s Right-Wing Alter Ego. 27 April. (https://www.wired.com/story/fast-forward-meet-chatgpts-right-wing-alter-ego/)

A few months ago I suggested it was time to apply WPR to WPR. I have thought of undertaking this project on numerous occasions. I was prompted into action by several WPR applications that have picked up the suggestion to apply WPR to contrasting theoretical perspectives (Bacchi 2009: 128-136; 103-105; 249-251; Research Hub entry 18 March 2018).

For example, Primdahl et al. (2018) reflect on the forms of critical analysis produced in some selected articles contributed to the Journal of Curriculum Studies. To undertake an analysis of the “content of the argumentation”, the authors examine the various contributions in terms of their problematizations, in effect applying WPR to the selected articles (see Research Hub entries 3 December 2018; 17 Dec. 2018). More recently, Johansson and Larsson (2022) use WPR to produce a critical review of the research literature on identity in university physics. These examples highlight the usefulness of applying WPR to theoretical propositions, opening up the opportunity to apply WPR to WPR. 

The decision to apply WPR to WPR was also motivated by the recognition that, as researchers, we are inside the processes we are examining. In line with the so-called “reflexive turn” in poststructuralist critique, it is necessary, therefore, to acknowledge that the researcher/theorist plays an active role in constructing the very reality s/he is attempting to investigate (Eveline and Bacchi 2010: 154). It is time, therefore, to ask myself just what “reality” WPR creates. 

What does WPR propose?

Applying WPR involves selecting specific proposals to gain access to the problematizations at work in the analytic target, in this case WPR (Research Hub entry 30 Jan. 2023). I decided to use my 2012 article “Introducing the ‘What’s the Problem Represented to be?’ approach” for this purpose since, by its nature (short, introductory), it is both simple and clear (Bacchi 2012: 21-24). By the same token, given its brevity, it lacks nuance and depth. I have always prided myself on an ability to “reduce” complex theory to accessible prose. Today, I remind myself that “pride cometh before a fall”! On this note I apologize in advance for the many references to my publications, which became inevitable in this attempt to problematize WPR. 

The short, simple Introduction to WPR drafted in 2012 reminded me of a critical article I wrote on a 2001 directive from the Canadian Health Services Research Foundation to health researchers to simplify their analyses to make them “useful” to policymakers. In this directive researchers are:

“Instructed to use plain English and to avoid jargon; they are to lay out their research using a 1:3:25 format, with one page of ‘main messages’, three pages of ‘executive summary’ and twenty-five pages for the main report.” (in Bacchi 2008: 169-170)

In my article on this directive I make the point that condensing the analysis to fit on one page potentially distorts the content. In support of this proposition, Mills and Thomson (2023: 193) describe how the sociological perspectives they, as consultants, sought to bring to the analysis of exclusion in UK education policy “were side-lined in the executive summary”. Clearly, then, the format we adopt to “convey” our “messages” – and this applies equally to my short Introduction to WPR – have power effects, which ought to be acknowledged.

In this Research Hub entry I treat the WPR set of questions (Bacchi and Goodwin 2016: 20) as a “practical text” and a guide to “conduct” (Bacchi 2009: 34). The brief Introduction, just mentioned (Bacchi 2012: 21), describes WPR as a “resource, or tool, intended to facilitate critical interrogation of public policies”.  Readers are informed that “this task is accomplished through a set of six questions and an accompanying undertaking to apply the questions to one’s own proposals for change”. While the question format suggests that the approach is not prescriptive, there is still the implication that WPR produces an organized way to proceed that ought to be followed. The power implications of this form of analysis need to be recognized. 

These power implications are most obvious in what Sue Goodwin and I (2016: 20) now refer to as “Step 7”, which calls upon researchers to engage in self-problematization: “Apply this list of questions to your own problem representations”. In contrast to the other six questions in the approach, “Step 7” is not a question but an instruction. Over the years I have struggled to find a term to soften the blow, if you will, making Step 7 more of a recommendation than a command. You will find the “instruction” described in different places as a “directive” (Bacchi 2009: 19; judged to be too strong), an “injunction” (blurb on back cover of Bacchi 2009; still too strong), an “undertaking” (Bacchi and Goodwin 2016: 19; just right?). I feel a little bit like Goldilocks! 

An undertaking is described as a task that is (simply) taken on, though it also carries the implication of a formal promise (Cambridge Dictionary). I judge the term to be “just right” since it appears to throw the ultimate decision to engage in self-problematisation, which is what Step 7 entails, back onto the researcher. You can see here my struggle to balance the power effects of WPR with a determination to encourage self-problematization. In the most recent iteration of the WPR “method”, I decide that encouraging self-problematisation ought to be the priority, for reasons elaborated later:

“In terms of practical application of WPR, it is possible to draw selectively upon the forms of questioning and analysis just described, so long as a self-problematizing ethic is maintained” (Bacchi and Goodwin 2016: 24; emphasis added). 

What is produced as “the problem”?

Given that the proposal is to adopt the WPR questions as a guide to research, the “problem” is produced as alternative ways of thinking (e.g. positivism). As a result, WPR is automatically engaged in epistemological and ontological debates. Following Question 2 in WPR, I ask: which epistemological and ontological presuppositions underlie the identified problem representation in WPR?

In several places (e.g., Bacchi and Goodwin 2016: 15, 33, 49) I, with Sue Goodwin, specify that WPR is associated with an ontology of becoming rather than an ontology of being. A clear distinction is drawn with realist premises. In an ontology of becoming, assumptions about the being of “things” are replaced by references to their becoming (Chia 1996). The physicality of “objects” is not questioned. However, “objects” are seen as in continuous development, as “in formation”, rather than as fixed. In effect, practices and relations replace “objects” (Veyne 1997). “Things” become open-ended and malleable. The argument is that such an ontological stance creates space for contestation and unmaking.

“Subjects” too are considered to be in a process of continuous development. In this argument, the idea that human beings are sovereign subjects who grasp meaning intuitively (Foucault 1972: 227) is rejected. This questioning of the humanist subject is contested in many quarters.

In terms of epistemology, a particular way of thinking about “knowledge” is at work in WPR. Following Foucault, knowledge is not “truth” but what is “in the true”, what is accepted as true (Bacchi and Goodwin 2016: 31, 35). Knowledges, or discourses, are treated as contingent historical creations that produce hierarchical and inegalitarian forms of rule. The focus of analysis becomes how “truths” are produced. 

This position is best described as anti-epistemological rather than as endorsing a specific epistemology. However, WPR makes a knowledge claim in its starting premise: “what one proposes to do about something reveals what one thinks is problematic (needs to change)” (Bacchi 2012: 21). This statement/claim displays a form of logic, or correct reasoning. At the same time, WPR criticizes forms of analysis that appear to claim “truth status”. Classically, it distinguishes itself from those forms of “ideology critique” that talk about “false consciousness”, implying the possibility of a “true consciousness” (discussion below). 

How does WPR negotiate this tension between making a truth claim and questioning claims to truth? The role of Step 7 becomes critical here. Through this analytic intervention (Step 7), WPR puts into question its own knowledge claims. 

How did we get “here” from “there”?

Since it is infeasible to produce a full-blown genealogy of WPR (Question 3), I have decided simply to list what would need to be included in such a genealogy, with some indications of issues that require elaboration. 

The short Introduction (Bacchi 2012), which I am using as our starting point, indicates that WPR has changed since its incarnation in the 1999 book, Women, Policy and Politics: the construction of policy problems (Bacchi 1999). Questions 3 and 6 were added in 2009 (Bacchi 2009: 2). Moreover, in 2012, I emphasized the need to refer to the approach as “What’s the Problem Represented to be?” rather than “What’s the problem?”, which I had used in 1999. To capture the fluid nature of WPR – how it changes over time –  the 2012 Introduction describes the “WPR” approach as an “open-ended mode of critical engagement, rather than as a formula”.

A genealogy would need to probe the alterations made to WPR mentioned in the 2012 Introduction and subsequent amendments to the approach to see what they reveal about the influences affecting the development of WPR. To my knowledge only one scholar has undertaken this task, Daniel Chao (2019), and he contributes significantly to locating WPR theoretically in terms of its shifting iterations. 

As one example, in the 1999 book I talk about WPR as a study of competing interpretations of “problems”, a perspective supported in the use of the term “construction” in the title – “the construction of policy problems” (Bacchi 1999: 2). The use of that term reflects the reliance at that time on social construction theorists (Bacchi 1999: 52-57).

By contrast the 2012 brief Introduction (p. 22) describes policies as producing “‘problems’ with particular meanings that affect what gets done or not done, and how people live their lives”. This move away from social constructionism and interpretivism to a focus on the constitutive effects of problem representations is strengthened in subsequent iterations. In my keynote address at the Karlstad Symposium I characterized this shift as a move away from social constructionism towards embracing performativity (see  KEYNOTE ADDRESS – CAROL BACCHI – 18 August 2022). 

The shift in WPR from a constructionist to a performative theoretical stance indicates the need to include a genealogical survey of critical theories through the period of writing. I would ask: which authors are identified as contributing to the thinking in WPR and what are their theoretical commitments? 

Clearly, Foucault becomes a major focus. While he is not explicitly mentioned in 2012, the articles referenced in the brief Introduction include one with Jennifer Bonham on “discursive practices”, a key concept in Foucault (2014), and one with Malin Rönnblom, on “discursive institutionalism” (2014), which contrasts a Foucauldian approach to other theoretical stances on “institutions”. 

Foucault clearly increases in influence in the evolution of WPR. The 2009 book, Analysing Policy: What’s the Problem Represented to be?, contains numerous quotes from Foucault in shaded boxes, from early in the text, to illustrate specific theoretical points (Bacchi 2009: xv, xix, 17, 26, 27, etc.). By 2016, with Sue Goodwin, I describe WPR as “a Foucault-influenced poststructural approach” (Bacchi and Goodwin 2016: 28) and note the importance of identifying “distinctions from other intellectual traditions”. The next genealogical task would entail identifying those distinctions.

Such a task immerses the researcher in the theoretical debates that have engaged those on the left politically from at least the time of Marx and Engels. It would involve developing a familiarity with the Frankfurt school, with Gramsci, with Laclau & Mouffe, with Latour, with Žižek, and so on. It would also necessitate clarifying the distinction between social constructivism, social constructionism and performativity (Bacchi 2009: 32-33).  

The debates around “ideology critique” and “false consciousness” help to locate WPR thinking in terms of theoretical developments (Research Hub 30 Sept 2021; 30 October 2021). The issue of concern became how to “understand how relations of domination or subordination are reproduced with only minimal resort to direct coercion” (Purvis and Hunt 1993: 474). Where Marx and Engels target the “distorted beliefs intellectuals held about society and the power of their own ideas” (Eyerman 1981: 43), post-Marxists, including Gramsci, Althusser and the early Frankfurt School (Daldal 2014: 157) are more concerned with the “false consciousness” of the working class. In the latter explanation, “false consciousness” serves as a convenient explanation “for the reluctance of oppressed workers to rise in revolt” (Strickland 2012: 48).

Displaying its Foucauldian sympathies, WPR questions the diagnosis of “false consciousness”, which presumes the ability to identify “true consciousness”. The pivotal place of self-problematization in WPR, indicated in Step 7, counters any such impression of epistemic privilege. In Foucauldian-influenced analyses, such as WPR, there is a shift in focus from the grand theorizing of a force called ideology to the minutiae of routine and mundane practices (Bacchi and Goodwin 2016). What is at stake here are contrasting versions of political change, as John Law (2008: 637) explains: 

“It is to refuse to be overawed by seemingly large systems, and the seeming ontological unity of the world enacted by large systems. It is, instead, to make the problem smaller, or better, to make it more specific.”

Disputes over the adequacy of this diagnosis/prognosis lie at the heart of many contemporary theoretical debates. 

What remains unproblematized in this problem representation?

Numerous scholars have pointed to topics and perspectives that they argue ought to be included in WPR. They often deal with these perceived lapses by combining WPR with other theoretical approaches. A central concern is that the focus on “routine and mundane practices” (see above) precludes critiques of large and patterned forces of structural oppression (Van Aswegan et al. 2019: 195). Another complaint is that the decentred subject in Foucauldian-influenced forms of analysis denies actors’ agency and bypasses social actors’ “everyday life and practice” (Brady 2014: 27). 

Questions are also raised about how WPR engages with important theoretical debates around feminism, disability, racism, ethnicity, sexual orientation and post-coloniality. The 2009 book identifies “feminist body theory” as a key intellectual tradition in WPR (Bacchi 2009: 264). No such reference appears in the 2016 book, though the text reflects on the engagement between poststructuralism and feminism (Bacchi and Goodwin 2016: 10-11, 42) and considers how “gender equality” is produced as a particular sort of “problem” in specific sites (pp. 65, 103). Researchers have productively explored the need to combine WPR with Critical Race Theory (Street et al. 2021), Critical Disability Theory (Van Aswegan et al.,2019), Critical Sexuality Studies (Pienaar et al. 2018; Manlik 2020), and post-colonial theory (Dixit and Banday 2022). 

What effects accompany this problem representation?

In the reluctance to address structural forms of oppression, WPR is considered by some to be involved in narrowing the terms of reference of critical analysis in unproductive ways. There is also widespread concern with the subjectification effects of the non-humanist subject presumed in WPR – specifically, the questioning of human agency. This concern translates into a conviction that poststructural forms of analysis create people as dupes of “the system”, diminishing the value of people’s lives. At a practical level, the reform implications (“lived effects”) of WPR are often considered weak, if not defeatist. The unwillingness to commit to specific reform agendas is held to undermine the possibility of progressive change (Savage et al.2021). 

Poststructuralists themselves express hesitation about the practical effects of reflexive research practices. Since meaning in poststructuralism can be fixed only temporarily, Chia (1996: 49) insists that “our own theoretical products must be self-deconstructing”. He states that “throw-away explanations are the essence of reflexive practice”, limiting the political utility of interventions such as WPR. 

Which specific practices produce this problem representation? And what forms of resistance are identifiable? (Question 6)

WPR has emerged from knowledge practices primarily in Western universities and through the contributions of a community of scholars based in these universities. Sue Goodwin and I (2016: 11) also stress the role of policy workers/analysts engaged in deploying WPR. Together, these researchers have expressed disquiet with contemporary political practices around issues to do with gender, race, disability, economic inequality, and heteronormativity.

From those who position themselves on the left politically, concern is expressed that poststructuralism weakens the political offensive against capitalism and its “vested interests”. From the right, the questioning of the rational agent is identified as a threat to human progress and Enlightenment. 

How can I apply self-problematization to an exercise in self-problematization? Step 7 – What is the point of this exercise? 

Subjecting WPR to a WPR analysis allows us to see that every research enterprise is an exercise in power relations. No such exercise is innocent. WPR encourages a particular way of thinking. That way of thinking challenges realist premises. It also puts under erasure a humanist conception of the subject. 

The extent to which these perspectives make it difficult to identify and confront groups and individuals who display intentional malfeasance in political encounters is a matter of contention. There is also a need to consider the adequacy, in terms of political response, of analysing the “conditions of emergence, insertion and functioning” of “regimes of truth” (Foucault 1972: 163). 

To apply Step 7 to this analysis, I need to consider if, as a critic, I may have missed something. Attention can be drawn to the determination in WPR to disrupt the power effects one inevitably creates (see discussion above). While the approach accepts the inevitability of “fixing” meanings, if only temporarily, it also creates space to question (“unfix”) those meanings, illustrating what Lather describes as a “doubled practice” (Bacchi and Eveline 2019: 340; Lather 2001). Researchers are prompted to see themselves as politically invested cultural beings who examine critically the analytic categories they adopt (Bacchi and Eveline 2010: 342). In this way, scepticism about the truths we produce and those we critique becomes a productive political force for change. 

As with the application of WPR in other sites, applying its questions to WPR itself ensures that an ongoing process of problematization and self-problematization is maintained. As we are all implicated in power relations and as our work necessarily has power effects, this recursive practice becomes essential.

References

Bacchi, C. 1999. Women, Policy and Politics: The construction of policy problems.London: Sage. 

Bacchi, C. 2008. The politics of research management: Reflections on the gap between what we “know” (about SDH) and what we do. Health Sociology Review, 17(2): 165-176.

Bacchi, C. 2009. Analysing Policy: What’s the Problem Represented to be? Frenchs Forest: Pearson Education.

Bacchi, C. 2012. Introducing the “What’s the Problem Represented to be?” approach. In A. Bletsas and C. Beasley (Eds) Engaging with Carol Bacchi: Strategic Interventions and Exchanges. Adelaide: University of Adelaide Press. pp. 21-24.

Bacchi, C. & Bonham, J. 2014. Reclaiming discursive practices as an analytic focus: Political implications. Foucault Studies, 17 (March): 173-192.

Bacchi, C. and Eveline, J. 2010. Conclusion: A Politics of Movement. In C. Bacchi and J. Eveline, Mainstreaming Politics: Gendering practices and feminist theory.Adelaide: University of Adelaide Press. pp. 139-161. pp. 335-343. 

Bacchi, C. and Goodwin, S. 2016. Poststructural Policy Analysis: A Guide to Practice. NY: Palgrave Macmillan.

Bacchi, C. & Rönnblom, M. (2014). Feminist Discursive Institutionalism – A Poststructural Alternative. NORA – Nordic Journal of Feminist and Gender Research, 22(3): 170-186.

Brady, M. 2014. Ethnographies of Neoliberal Governmentalities: from the neoliberal apparatus to neoliberalism and governmental assemblages. Foucault Studies, 18: 11-33.

Chia, R. 1996. The problem of reflexivity in organisational research. Organization, 3(1): 31-59.

Chao, D. 2019. Problematizaciones, problemas representados y gubernamentalidad. Una propuesta analítica para el estudio de las políticas públicas y el estado.

(Problematizations, problems represented and governmentality. An analytical proposal for the study of public policies and the state). De Prácticas y discursos Universidad Nacional del Nordeste Centro de Estudios Sociales, 8(11): 123-152. DOI:10.30972/dpd.8113807

Open access at: http://revistas.unne.edu.ar/index.php/dpd/article/view/3807/3428

Daldal, A. 2014. Power and Ideology in Michel Foucault and Antonio Gramsci: A Comparative Analysis. Review of History and Political Science, 2(2): 149-167.

Dixit, A. and Banday, M. U. L. 2022. Problematising the digital gender gap: invoking decoloniality and intersectionality for inclusive policymaking, Gender & Development, 30:3, 437-457, DOI: 10.1080/13552074.2022.2117930 

Eveline, J. and Bacchi, C. 2010. Power, resistance and reflexive practice. In C. Bacchi and J. Eveline, Mainstreaming Politics: Gendering practices and feminist theory. Adelaide: University of Adelaide Press. pp. 139-161. 

Eyerman, R. 1981. False Consciousness and Ideology in Marxist Theory. Acta Sociologica, 24(1-2): 43-56. 

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